% Part I - Recommending Bookmarks

\part{Recommending Bookmarks}
\label{part1}
\lhead{Part I. \emph{Recommending Bookmarks}}

In Chapter \ref{chapter1}, we described two important characteristics of social bookmarking systems. The first was the presence of the folksonomy, a collaboratively generated categorization of items in a system by its users. This extra annotation layer of collaboratively generated tags binds the users and items of a system together. The second observation about social bookmarking systems is that, in addition to tags, they contain a large amount of additional metadata describing the content of those systems and its users. In the first part of this thesis, we will investigate how to exploit these two characteristics, both separately and combined, to provide the best possible recommendations for users of social bookmarking systems. This leads us to our first three research questions.

\begin{center}\begin{tabularx}{0.9\linewidth}{lX}
  {\bf RQ 1}   & How can we use the information represented by the folksonomy 
                 to support and improve the recommendation performance? \\
  ~            & ~ \\
  {\bf RQ 2}   & How can we use the item metadata available in social 
                 bookmarking systems to provide accurate recommendations to 
                 users? \\
  ~            & ~ \\
  {\bf RQ 3}   & Can we improve performance by combining the recommendations 
                 generated by different algorithms? \\
\end{tabularx}\end{center}

Part \ref{part1} is organized as follows. We begin in Chapter \ref{chapter3}, where we present the building blocks of our quantitative experimental evaluation. We formally define our recommendation task, and introduce our data sets. We discuss the filtering we applied to the data, our experimental setup, and discuss the evaluation metrics we use. We also critically discuss the pros and cons of our methodological decisions. In Chapter \ref{chapter4}, we propose and compare different options for using the folksonomy for item recommendation on social bookmarking websites (RQ 1). Chapter \ref{chapter5} examines how we can use the metadata present in social bookmarking systems to improve recommendation performance (RQ 2). Chapter \ref{chapter6} concludes Part \ref{part1} and investigates if we can improve upon our best-performing algorithms from Chapters \ref{chapter4} and \ref{chapter5} by combining the output of the different algorithms into a new list of recommendations (RQ 3).
